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Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies.
One-class classifier networks for target recognition applications
M. M. Moya, M. W. Koch, and L. D. Hostetler · 1993
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Estimating the support of a high-dimensional distribution
Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alex J. Smola, and Robert C. Williamson · 2001
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Mnist handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Learning and transferring mid-level image representations using convolutional neural networks
Maxime Oquab, Léon Bottou, Ivan Laptev, and Josef Sivic · 2014
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2014
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Variational autoencoder based anomaly detection using reconstruction probabiliy
Jinwon An and Sungzoon Cho · 2015
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Transfer representation-learning for anomaly detection
Jerone T. A. Andrews, Thomas Tanay, Edward J. Morton, and Lewis D. Griffin · 2016
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High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning
Sarah M. Erfani, Sutharshan Rajasegarar, Shanika Karunasekera, and Christopher Leckie · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Identifying and categorizing anomalies in retinal imaging data
Philipp Seeböck, Sebastian Waldstein, Sophie Klimscha, Bianca S. Gerendas René Donner, Thomas Schlegl, Ursula Schmidt-Erfurth, and Georg Langs · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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A revisit of sparse coding based anomaly detection in stacked rnn framework
Weixin Luo, Wen Liu, and Shenghua Gao · 2017
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Ursula Schmidt-Erfurth, and Georg Langs · 2017
Cited alongside, same era.
Grad-CAM: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, and Devi Parikh · 2017
Cited alongside, same era.
Anomaly detection with robust deep autoencoders
Chong Zhou and Randy C. Paffenroth · 2017
Cited alongside, same era.
GANomaly: Semi-supervised anomaly detection via adversarial training
Samet Akcay, Amir Atapour-Abarghouei, and Toby P. Breckon · 2018
Cited alongside, same era.
Clustering and unsupervised anomaly detection with l 2 l_{2} normalized deep auto-encoder representations
Caglar Aytekin, Xingyang Ni, Francesco Cricri, and Emre Aksu · 2018
Cited alongside, same era.
Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen · 2018
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Latent space autoregression for novelty detection
Davide Abati, Angelo Porrello, Simone Calderara, and Rita Cucchiara · 2019
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Mvtec AD - A comprehensive real-world dataset for unsupervised anomaly detection
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2019
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Where’s wally now? deep generative and discriminative embeddings for novelty detection
Philippe Burlina, Neil Joshi, and I-Jeng Wang · 2019
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Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2019
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Deep autoencoding models for unsupervised anomaly segmentation in brain mr images
Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, and Nassir Navab · 2018
Cited alongside, same era.
Anomaly detection using one-class neural networks
Raghavendra Chalapathy, Aditya Krishna Menon, and Sanjay Chawla · 2018
Cited alongside, same era.
Image anomaly detection with generative adversarial networks
Lucas Deecke, Robert Vandermeulen, Lukas RuffStephan Mandt, and Marius Kloft · 2018
Cited alongside, same era.
Deep anomaly detection using geometric transformations
Izhak Golan and Ran El-Yaniv · 2018
Cited alongside, same era.
Metric learning for novelty and anomaly detection
Marc Masana, Idoia Ruiz, Joan Serrat, Van De Weijer Joost, and Antonio M Lopez · 2018
Cited alongside, same era.
Anomaly detection in nanofibrous materials by CNN-based self-similarity
Paolo Napoletano, Flavio Piccoli, and Raimondo Schettini · 2018
Cited alongside, same era.
Informed democracy: Voting-based novelty detection for action recognition
Alina Roitberg, Ziad Al-Halah, and Rainer Stiefelhagen · 2018
Cited alongside, same era.
Michael Fauser David Sattlegger Paul Bergmann, Sindy Löwe and Carsten Steger · 2019
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f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Georg Langs, and Ursula Schmidt-Erfurth · 2019
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Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2020
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Sub-image anomaly detection with deep pyramid correspondences
Niv Cohen and Yedid Hoshen · 2020
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Attribute restoration framework for anomaly detection
Chaoqin Huang, Fei Ye, Jinkun Cao, Maosen Li, Ya Zhang, and Cewu Lu · 2020
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Towards visually explaining variational autoencoders
Wenqian Liu, Runze Li, Meng Zheng, Srikrishna Karanam, Ziyan Wu, Bir Bhanu, Richard J Radke, and Octavia Camps · 2020
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Attention guided anomaly localization in images
Shashanka Venkataramanan, Kuan-Chuan Peng, Rajat Vikram Singh, and Abhijit Mahalanobis · 2020
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Lin Wang and Kuk-Jin Yoon · 2020
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Patch svdd: Patch-level svdd for anomaly detection and segmentation
Jihun Yi and Sungroh Yoon · 2020
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Padim: A patch distribution modeling framework for anomaly detection and localization
Thomas Defard, Aleksandr Setkov, Angelique Loesch, and Romaric Audigier · 2021
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Cutpaste: Self-supervised learning for anomaly detection and localization
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister · 2021
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